The main aim of this research is to compare the results of the study of demand’s plan and

standardized time based on three heuristic scheduling methods such as Campbell Dudek

Smith (CDS), Palmer, and Dannenbring. This paper minimizes the makespan under certain

and uncertain demand for domestic boxes at the leading glass company industry in Indonesia.

The investigation is run in a department called Preparation Box (later simply called PRP)

which experiences tardiness while meeting the requirement of domestic demand. The effect

of tardiness leads to unfulfilled domestic demand and hampers the production department

delivers goods to the customer on time. PRP needs to consider demand planning for the

next period under the certain and uncertain demand plot using the forecasting and Monte

Carlo simulation technique. This research also utilizes a work sampling method to calculate

the standardized time, which is calculated by considering the performance rating and

allowance factor. This paper contributes to showing a comparison between three heuristic

scheduling methods performances regarding a real-life problem. This paper concludes that

the Dannenbring method is suitable for large domestic boxes under certain demand while

Palmer and Dannenbring methods are suitable for large domestic boxes under uncertain

demand. The CDS method is suitable to prepare small domestic boxes for both certain and

uncertain demand.

Increased competition has led businesses to compete with each other in streamlining supply chain processes, especially in the manufacturing sector. Supply Chain Management (SCM) determines the success of industrial business processes because it regulates product flow regarding integration, performance, and information. However, several problems have emerged in the supply chain process, such as a lack of coordination in the production queue, difficulties in forecasting trending products, and suboptimal production capacity. To address these issues, the role of information technology is crucial for implementing a Decision Support System (DSS). This study aims to develop a DSS to improve the supply chain processes. The research method used is Extreme Programming (XP) with a qualitative approach through a questionnaire. The research process involves collecting data, defining boundaries and problems, and designing, coding, and testing the system. As a final step, evaluation is carried out by distributing surveys to obtain valid satisfaction results. This research produces a DSS that has applicability in marketing, accounting, and production processes. The application of DSS in the furniture manufacturing industry can help manage the movement of resources, optimize strategic networks, and assist decision-making in the supply chain process.

Supply Chain Management (SCM) is a very important part of the industrial world, especially in the manufacturing sector. The development of the business world affects the complexity of the supply chain due to the lack of logistics infrastructure, quality of materials and components, and much more. Supply chain disruption risk mapping needs to be done due to high uncertainty, which is overcome by implementing a decision support system. Based on the background of the problem, supply chain disruption mapping uses the help of the Six Sigma method, which consists of 5 stages: Define, Measure, Analyze, Improve, and Control (DMAIC). The measurement of disturbance also uses the Failure Mode and Effect Analysis (FMEA) approach to prioritize risk. Risks that have a high assessment and cause failure need to be prioritized for improvement. This study aims to map supply chain disruptions in the current manufacturing industry based on the barriers, resistances, and causes detected for making a decision support system prototype. By implementing a decision support system in the supply chain process, it is

Improper planning of inventory will affect the factory operating costs, building costs, the cost of loss, and the cost of product defects due to being stored for too long which will eventually become a loss. This research discusses the processing industry which is experiencing lumpy demand. In carrying out the production process, the company has never made plans for future demand, resulting in a waste of message costs due to repeated orders of raw materials ordered to suppliers. This paper contributes to overcoming this issue by simulating future demand by using the Material Requirement Planning (MRP) method with a probabilistic Economic Order Quantity (EOQ) and Periodic Order Quantity (POQ) model. The demand in the coming period is determined using the Autoregressive Integrated Moving Average (ARIMA) method, and an aggregate plan is carried out to determine the regular cost of raw material production and optimal subcontracting. The final analysis states that the calculation of MRP on the selected items using POQ produces the lowest cost for planning S45C-F, SGT-R, and SKD11-R, while SLD-R uses the probabilistic EOQ method.

The objective of this research is to minimize product defects based on labor performance and prove the hypothesis on how labor performance affects the quality of a product through a scientific calculation using Overall Labor Effectiveness (OLE). The primary data is obtained by interviewing the supervisor and labor directly. For secondary data is obtained from the company, such as labor working time, machine scheduled downtime, total production, and defective products. The approach to extract the data is using OLE and the continued regression method. Furthermore, it proceeds to Six Sigma using the DMAIC approach since the results show a significant correlation. The result from Failure Mode and Effects Analysis (FMEA) shows four of six potential failures caused by product defects are coming from labor. To prevent failure mode, it is recommended to have the regular machine checked by labor, check the temperature of the machine, and provide Standard Operating Procedures.

This study was conducted in a company that produces palm oil-based products such as cooking oil and margarine. The study aimed to encounter defects in packaging pouches. This study integrated the overall equipment effectiveness (OEE) with the six sigma DMAIC method. The OEE was performed to measure the efficiency of the machine. Three factors were measured in OEE: availability, performance, and quality. These factors were calculated and compared to the OEE world-class value. Then, the Multiple Linear Regression was performed using SPSS to determine the correlation between measurement variables toward the OEE value. Lastly, the six sigma method was implemented through the DMAIC approach to find the solution and improve the packaging quality. Supposing the recommendations are implemented, the OEE is expected to increase from 82% to 85%, with availability ratio, performance ratio, and quality ratio at, 99%, 86%, and 99.8%, respectively.

This study uses statistical quality control (SQC) and overall equipment effectiveness (OEE) to examine quality at a porcelain production firm. The study is motivated by the most frequently broken machines in 2019, is the Jigger 01 machine. This paper aims to evaluate the machine’s effectiveness using the OEE method. The OEE determines the scope of the problem to be solved using the SQC method. The average OEE value in 2019 was 70%. Based on the SQC method, the product defect produced is still under control. However, the average defect is still above the company’s tolerance limit of 10%. Consequently, this study offers enhancements utilizing the Failure Mode Effect Analysis (FMEA) technique. The results indicate that human resources and machines caused defective products. This paper contributes to providing several improvements that the company can apply to maximize its quality control analysis. After implementing the improvement, the OEE value increases to 74%.